Investigation of daily patterns for smartphone keystroke dynamics based on loneliness and social isolation.

This study examined the relationship between loneliness levels and daily patterns of mobile keystroke dynamics in healthy individuals. Sixty-six young healthy Koreans participated in the experiment. Over five weeks, the participants used a custom Android keyboard. We divided the participants into four groups based on their level of loneliness (no loneliness, moderate loneliness, severe loneliness, and very severe loneliness). The very severe loneliness group demonstrated significantly higher typing counts during sleep time than the other three groups (one-way ANOVA, F = 3.75, p < 0.05). In addition, the average cosine similarity value of weekday and weekend typing patterns in the very severe loneliness group was higher than that in the no loneliness group (Welch's t-test, t = 2.27, p < 0.05). This meant that the no loneliness group's weekday and weekend typing patterns varied, whereas the very severe loneliness group's weekday and weekend typing patterns did not. Our results indicated that individuals with very high levels of loneliness tended to use mobile keyboards during late-night hours and did not significantly change their smartphone usage behavior between weekdays and weekends. These findings suggest that mobile keystroke dynamics have the potential to be used for the early detection of loneliness and the development of targeted interventions.

Effect of temporal interference electrical stimulation on phasic dopamine release in the striatum.

BACKGROUND: Temporal interference stimulation (TIS) is a neuromodulation technique that could stimulate deep brain regions by inducing interfering electrical signals based on high-frequency electrical stimulations of multiple electrode pairs from outside the brain. Despite numerous TIS studies, however, there has been limited investigation into the neurochemical effects of TIS. OBJECTIVE: We performed two experiments to investigate the effect of TIS on the medial forebrain bundle (MFB)-evoked phasic dopamine (DA) response. METHODS: In the first experiment, we applied TIS next to a carbon fiber microelectrode (CFM) to examine the modulation of the MFB-evoked phasic DA response in the striatum (STr). Beat frequencies and intensities of TIS were 0, 2, 6, 10, 20, 60, 130 Hz and 0, 100, 200, 300, 400, 500 µA. In the second experiment, we examined the effect of TIS with a 2 Hz beat frequency (based on the first experiment) on MFB-evoked phasic DA release when applied above the cortex (with a simulation-based stimulation site targeting the striatum). We employed 0 Hz and 2 Hz beat frequencies and a control condition without stimulation. RESULTS: In the first experiment, TIS with a beat frequency of 2 Hz and an intensity of 400 µA or greater decreased MFB-evoked phasic DA release by roughly 40%, which continued until the experiment's end. In contrast, TIS at beat frequencies other than 2 Hz and intensities less than 400 µA did not affect MFB-evoked phasic DA release. In the second experiment, TIS with a 2 Hz beat frequency decreased only the MFB-evoked phasic DA response, but the reduction in DA release was not sustained. CONCLUSIONS: STr-applied and cortex-applied TIS with delta frequency dampens evoked phasic DA release in the STr. These findings demonstrate that TIS could influence the neurochemical modulation of the brain.

Feasibility of epidural temporal interference stimulation for minimally invasive electrical deep brain stimulation: simulation and phantom experimental studies.

Objective. Temporal interference stimulation (TIS) has shown the potential as a new method for selective stimulation of deep brain structures in small animal experiments. However, it is challenging to deliver a sufficient temporal interference (TI) current to directly induce an action potential in the deep area of the human brain when electrodes are attached to the scalp because the amount of injection current is generally limited due to safety issues. Thus, we propose a novel method called epidural TIS (eTIS) to address this issue; in this method, the electrodes are attached to the epidural surface under the skull.Approach. We employed finite element method (FEM)-based electric field simulations to demonstrate the feasibility of eTIS. We first optimized the electrode conditions to deliver maximum TI currents to each of the three different targets (anterior hippocampus, subthalamic nucleus, and ventral intermediate nucleus) based on FEM, and compared the stimulation focality between eTIS and transcranial TIS (tTIS). Moreover, we conducted realistic skull-phantom experiments for validating the accuracy of the computational simulation for eTIS.Main results. Our simulation results showed that eTIS has the advantage of avoiding the delivery of TI currents over unwanted neocortical regions compared with tTIS for all three targets. It was shown that the optimized eTIS could induce neural action potentials at each of the three targets when a sufficiently large current equivalent to that for epidural cortical stimulation is injected. Additionally, the simulated results and measured results via the phantom experiments were in good agreement.Significance. We demonstrated the feasibility of eTIS, facilitating more focalized and stronger electrical stimulation of deep brain regions than tTIS, with the relatively less invasive placement of electrodes than conventional deep brain stimulation via computational simulation and realistic skull phantom experiments.

Non-human primate epidural ECoG analysis using explainable deep learning technology.

Objective.With the development in the field of neural networks,explainable AI(XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results.Approach. We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment.Main results. The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements.Significance.As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance.

The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance.

Numerous brain-machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI.

Comparison between Concentration and Immersion Based on EEG Analysis.

Concentration and immersion belong to a similar mental state in which a person is preoccupied with a particular task. In this study, we investigated a possibility of diagnosing two mental states with a subtle difference. Concentration and immersion states were induced to analyze the electroencephalography (EEG) changes during these states. Thirty-two college students in their 20s participated in the study. For concentration, subjects were asked to focus on a red dot at the center of a white screen, and for immersion they were asked to focus on playing a computer game. Relative to rest, Alpha waves decreased during concentration and immersion. Relative to rest, Theta waves decreased at almost all channels during concentration and, on the other hand, increased at all channels during immersion. Beta waves increased during concentration and immersion in the frontal and occipital lobes, with a higher increase in immersion. In the temporal lobe, Beta waves decreased during concentration and increased during immersion. In the central region, Beta waves decreased during concentration and immersion, and the decrease during immersion was larger. Such evident differences between the EEG results for concentration and immersion can imply diagnostic capabilities of various other mental states.

Long-term evaluation and feasibility study of the insulated screw electrode for ECoG recording.

BACKGROUND: A screw-shaped electrode can offer a compromise between signal quality and invasiveness. However, the standard screw electrode can be vulnerable to electrical noise while directly contact with the skull or skin, and the feasibility and stability for chronic implantation in primate have not been fully evaluated. NEW METHOD: We designed a novel screw electrocorticogram (ECoG) electrode composed of three parts: recording electrode, insulator, and nut. The recording electrode was made of titanium with high biocompatibility and high electrical conductivity. Zirconia is used for insulator and nut to prevent electrical noise. RESULT: In computer simulations, the screw ECoG with insulator showed a significantly higher performance in signal acquisition compared to the condition without insulator. In a non-human primate, using screw ECoG, clear visual-evoked potential (VEP) waveforms were obtained, VEP components were reliably maintained, and the electrode's impedance was stable during the whole evaluation period. Moreover, it showed higher SNR and wider frequency band compared to the electroencephalogram (EEG). We also observed the screw ECoG has a higher sensitivity that captures different responses on various stimuli than the EEG. COMPARISON: The screw ECoG showed reliable electrical characteristic and biocompatibility for three months, that shows great promise for chronic implants. These results contrasted with previous reports that general screw electrode was only applicable for acute applications. CONCLUSION: The suggested electrode can offer whole-brain monitoring with high signal quality and minimal invasiveness. The screw ECoG can be used to provide more in-depth understanding, not only relationship between functional networks and cognitive behavior, but also pathomechanisms in brain diseases.

Analysis of Biosignals During Immersion in Computer Games.

The number of computer game users is increasing as computers and various IT devices in connection with the Internet are commonplace in all ages. In this research, in order to find the relevance of behavioral activity and its associated biosignal, biosignal changes before and after as well as during computer games were measured and analyzed for 31 subjects. For this purpose, a device to measure electrocardiogram, photoplethysmogram and skin temperature was developed such that the effect of motion artifacts could be minimized. The device was made wearable for convenient measurement. The game selected for the experiments was League of Legends™. Analysis on the pulse transit time, heart rate variability and skin temperature showed increased sympathetic nerve activities during computer game, while the parasympathetic nerves became less active. Interestingly, the sympathetic predominance group showed less change in the heart rate variability as compared to the normal group. The results can be valuable for studying internet gaming disorder.